Classification of Scale-Free Networks

• Published in: Proceedings of the National Academy of Sciences - PNAS 2002-10, Vol.99 (20), p.12583-12588
• Main Authors: Goh, Kwang-Il, Oh, Eulsik, Jeong, Hawoong, Kahng, Byungnam, Kim, Doochul
• Format: Article
• Language: English
• Subjects: Archaea; Ascomycota - physiology; Classification; Eukaryotic cells; Fungal Proteins - chemistry; Internet; Metabolism; Models, Theoretical; Neural Networks, Computer; Physical Sciences; Physics; Physics - methods; Power laws; Proteins - chemistry; Saccharomyces cerevisiae - physiology; Stochastic models; Topology; Universality; Vertices; World Wide Web
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.202301299

While the emergence of a power-law degree distribution in complex networks is intriguing, the degree exponent is not universal. Here we show that the betweenness centrality displays a power-law distribution with an exponent η, which is robust, and use it to classify the scale-free networks. We have observed two universality classes with η ≈ 2.2(1) and 2.0, respectively. Real-world networks for the former are the protein-interaction networks, the metabolic networks for eukaryotes and bacteria, and the coauthorship network, and those for the latter one are the Internet, the World Wide Web, and the metabolic networks for Archaea. Distinct features of the mass-distance relation, generic topology of geodesics, and resilience under attack of the two classes are identified. Various model networks also belong to either of the two classes, while their degree exponents are tunable.